Global-to-local shape priors for variational image segmentation

One major problem, when using statistical shape information in image segmentation problems, is that many training samples are needed in order to obtain a satisfactory shape prior for a particular class, especially when the intra-class variability of the object shapes is high. To cope with this problem, we present an elegant variational formulation that allows local adaptations of the parameters associated with a trained shape prior. This enables us to obtain accurate segmentation results with a limited amount of training shapes. We provide a sound mathematical foundation for our approach and embed it into the well-known level set segmentation framework, which makes our approach applicable to a large class of problems. Moreover, we show how a smooth transition from global to local adaptations of the shape parameters can be achieved. We point out the advantages of our new variational global-to-local approach by comparing it with another level set segmentation approach that includes a global shape prior.